Repository logo
 

Faculty of Accounting and Informatics

Permanent URI for this communityhttp://ir-dev.dut.ac.za/handle/10321/1

Browse

Author: search.filters.author.Cele, Noxolo PrettyHas files: Yes

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Integration of an autoencoder model with an actor-oriented system
    (Advances in Artificial Intelligence and Machine Learning, 2024) Dyubele, Sithembiso; Cele, Noxolo Pretty; Mbangata, Lubabalo
    Traditional machine learning frameworks often struggle with scalability, modularity, and efficient resource management, especially when dealing with vast data. Actor-Oriented Systems offer a robust framework for building such scalable systems, allowing concurrent processing and efficient handling of large datasets. This study investigated the integration of Autoencoders (AE), which are pivotal in unsupervised learning, with Actor-Oriented Systems to enhance the modularity, scalability, and maintainability of the model training process. The study seeks to leverage the capabilities of AE and Actor-Oriented Systems to achieve high-quality image reconstruction and efficient processing. The study also attempted to understand the underlying patterns in the data, assess the performance of the model, and demonstrate the benefits of modular and scalable systems. Key findings from the results showed significant improvements in training efficiency and performance of the model, especially when using Actor-Oriented Systems. The training time was reduced from 16.96 seconds to 14.21 seconds, and the validation loss improved from 0.2768 to 0.2100, indicating better generalisation and learning. Data augmentation techniques further enhanced the robustness of the model, leading to more accurate reconstructions of the test images. Actor-Oriented Systems facilitated concurrent processing, improved modularity, and enabled the system to scale efficiently with increasing data volume. This study also highlighted the practical benefits of integrating AE with Actor-Oriented Systems, providing valuable insights into building more robust, maintainable, and scalable machine learning workflows.
  • Thumbnail Image
    Item
    Adoption of mobile cloud computing by small and medium-sized enterprises (SMEs) in rural areas
    (2023-10) Cele, Noxolo Pretty; Govender, Mogiveny; Rajkoomar, Mogiveny
    This study was conducted in the rural areas of KwaZulu-Natal (KZN) province of the Republic of South Africa. These rural areas includes uMbumbulu, Hlokozi, Eluphepheni, and KwaMakhutha. The main aim was to examine various factors that affect the adoption of Mobile Cloud Computing (MCC) by SMEs located in these areas. This study was motivated by benefits of MCC in various sectors around the world. This includes the provision of cloud-based services to users through the Internet and mobile devices. The current study is of the view that providing MCC to small and medium sized enterprises (SMEs) in rural areas can help them leverage cloud computing resources to improve their performance and delivery of services to customers. A quantitative research strategy was employed to obtain greater knowledge and understanding of the factors that affect the implementation of MCC by SMEs in the indicated rural areas, aiming to produce objective data that can be clearly communicated through statistics and numbers. The online survey was administered to owners, managers, employees and customers of these SMEs. The collected data was later analysed through Excel and the Statistical Package for the Social Sciences (SPSS) version 25. The results of this study reveal that, according to the customers’ point of view, SMEs in rural areas will be able to adopt MCC if there are adequate technological devices. The results suggest that relative advantage (RA), perceived security, perceived ease of use (PEU), and attitude are some of the factors that need to be considered for SMEs in rural areas to successfully adopt MCC. The findings also signal a strong correlation between perceived need, technological devices, compatibility, RA, complexity, trialability, and observability, when measured against MCC. In summary, the results indicate the importance of doing proper research before adopting cloud-based services in order to identify the need for MCC adoption. Significantly few respondents showed resistance or doubt regarding SMEs‘ adoption of MCC.